The Experts below are selected from a list of 82911 Experts worldwide ranked by ideXlab platform
Fang Liu - One of the best experts on this subject based on the ideXlab platform.
-
a note on a Goal Programming model for incomplete interval multiplicative preference relations and its application in group decision making
European Journal of Operational Research, 2012Co-Authors: Fang Liu, Weiguo Zhang, Zhongxing WangAbstract:Abstract In a recently published paper by Liu et al. [Liu, F., Zhang, W.G., Wang, Z.X. (2012). A Goal Programming model for incomplete interval multiplicative preference relations and its application in group decision-making. European Journal of Operational Research 218, 747–754], two equations are introduced to define consistency of incomplete interval multiplicative preference relations (IMPRs) and employed to develop a Goal Programming model for estimating missing values. This note illustrates that such consistency definition and estimation model are technically incorrect. New transitivity conditions are proposed to define consistent IMPRs, and a two-stage Goal Programming approach is devised to estimate missing values for incomplete IMPRs.
-
a Goal Programming model for incomplete interval multiplicative preference relations and its application in group decision making
European Journal of Operational Research, 2012Co-Authors: Fang Liu, Weiguo Zhang, Zhongxing WangAbstract:In decision making problems, there may be the cases where the decision makers express their judgements by using preference relations with incomplete information. Then one of the key issues is how to estimate the missing preference values. In this paper, we introduce an incomplete interval multiplicative preference relation and give the definitions of consistent and acceptable incomplete ones, respectively. Based on the consistency property of interval multiplicative preference relations, a Goal Programming model is proposed to complement the acceptable incomplete one. A new algorithm of obtaining the priority vector from incomplete interval multiplicative preference relations is given. The Goal Programming model is further applied to group decision-making (GDM) where the experts evaluate their preferences as acceptable incomplete interval multiplicative preference relations. An interval weighted geometric averaging (IWGA) operator is proposed to aggregate individual preference relations into a social one. Furthermore, the social interval multiplicative preference relation owns acceptable consistency when every individual one is acceptably consistent. Two numerical examples are carried out to show the efficiency of the proposed Goal Programming model and the algorithms.
Chingter Chang - One of the best experts on this subject based on the ideXlab platform.
-
multi choice Goal Programming model for the optimal location of renewable energy facilities
Renewable & Sustainable Energy Reviews, 2015Co-Authors: Chingter ChangAbstract:Abstract This paper proposes a multi-choice Goal Programming model for dealing with the capacity expansion planning problem of the renewable energy industry. This model involves decisions regarding the optimal mix of different plant types, location selection and other criteria. Different types of plants should be located in appropriate places so as to minimize the total deviations from predefined Goals concerning power generated, investment cost, emission avoided, jobs created, operation and maintenance costs, distance security, and social acceptance. The proposed method is superior to the Goal Programming model proposed by Ramon and Cristobal, in that it can avoid underestimation of aspiration level, expand the potential feasible region, and achieve findings more closely approach actual conditions. In addition, the social acceptance of the renewable energy planning problem in Taiwan is modeled by the MCGP to demonstrate its usefulness.
-
integrated multi choice Goal Programming and multi segment Goal Programming for supplier selection considering imperfect quality and price quantity discounts in a multiple sourcing environment
International Journal of Systems Science, 2014Co-Authors: Chingter Chang, Huangmu Chen, Zhengyun ZhuangAbstract:Supplier selection SS is a multi-criteria and multi-objective problem, in which multi-segment e.g. imperfect-quality discount IQD and price-quantity discount PQD and multi-aspiration level problems may be significantly important; however, little attention had been given to dealing with both of them simultaneously in the past. This study proposes a model for integrating multi-choice Goal Programming and multi-segment Goal Programming to solve the above-mentioned problems by providing the following main contributions: 1 it allows decision-makers to set multiple aspiration levels on the right-hand side of each Goal to suit real-world situations, 2 the PQD and IQD conditions are considered in the proposed model simultaneously and 3 the proposed model can solve a SS problem with n suppliers where each supplier offers m IQD with r PQD intervals, where only extra binary variables are required. The usefulness of the proposed model is explained using a real case. The results indicate that the proposed model not only can deal with a SS problem with multi-segment and multi-aspiration levels, but also can help the decision-maker to find the appropriate order quantities for each supplier by considering cost, quality and delivery.
-
multi choice Goal Programming with utility functions
European Journal of Operational Research, 2011Co-Authors: Chingter ChangAbstract:Abstract Goal Programming (GP) has been, and still is, the most widely used technique for solving multiple-criteria decision problems and multiple-objective decision problems by finding a set of satisfying solutions. However, the major limitation of Goal Programming is that can only use aspiration levels with scalar value for solving multiple objective problems. In order to solve this problem multi-choice Goal Programming (MCGP) was proposed by Chang (2007a) . Following the idea of MCGP this study proposes a new concept of level achieving in the utility functions to replace the aspiration level with scalar value in classical GP and MCGP for multiple objective problems. According to this idea, it is possible to use the skill of MCGP with utility functions to solve multi-objective problems. The major contribution of using the utility functions of MCGP is that they can be used as measuring instruments to help decision makers make the best/appropriate policy corresponding to their Goals with the highest level of utility achieved. In addition, the above properties can improve the practical utility of MCGP in solving more real-world decision/management problems.
-
binary fuzzy Goal Programming approach to single model straight and u shaped assembly line balancing
European Journal of Operational Research, 2009Co-Authors: Yakup Kara, Turan Paksoy, Chingter ChangAbstract:Assembly line balancing generally requires a set of acceptable solutions to the several conflicting objectives. In this study, a binary fuzzy Goal Programming approach is applied to assembly line balancing. Models for balancing straight and U-shaped assembly lines with fuzzy Goals (the number of workstations and cycle time Goals) are proposed. The binary fuzzy Goal Programming models are solved using the methodology introduced by Chang [Chang, C.T., 2007. Binary fuzzy Goal Programming. European Journal of Operational Research 180 (1), 29-37]. An illustrative example is presented to demonstrate the validity of the proposed models and to compare the performance of straight and U-shaped line configurations.
Zhongxing Wang - One of the best experts on this subject based on the ideXlab platform.
-
a note on a Goal Programming model for incomplete interval multiplicative preference relations and its application in group decision making
European Journal of Operational Research, 2012Co-Authors: Fang Liu, Weiguo Zhang, Zhongxing WangAbstract:Abstract In a recently published paper by Liu et al. [Liu, F., Zhang, W.G., Wang, Z.X. (2012). A Goal Programming model for incomplete interval multiplicative preference relations and its application in group decision-making. European Journal of Operational Research 218, 747–754], two equations are introduced to define consistency of incomplete interval multiplicative preference relations (IMPRs) and employed to develop a Goal Programming model for estimating missing values. This note illustrates that such consistency definition and estimation model are technically incorrect. New transitivity conditions are proposed to define consistent IMPRs, and a two-stage Goal Programming approach is devised to estimate missing values for incomplete IMPRs.
-
a Goal Programming model for incomplete interval multiplicative preference relations and its application in group decision making
European Journal of Operational Research, 2012Co-Authors: Fang Liu, Weiguo Zhang, Zhongxing WangAbstract:In decision making problems, there may be the cases where the decision makers express their judgements by using preference relations with incomplete information. Then one of the key issues is how to estimate the missing preference values. In this paper, we introduce an incomplete interval multiplicative preference relation and give the definitions of consistent and acceptable incomplete ones, respectively. Based on the consistency property of interval multiplicative preference relations, a Goal Programming model is proposed to complement the acceptable incomplete one. A new algorithm of obtaining the priority vector from incomplete interval multiplicative preference relations is given. The Goal Programming model is further applied to group decision-making (GDM) where the experts evaluate their preferences as acceptable incomplete interval multiplicative preference relations. An interval weighted geometric averaging (IWGA) operator is proposed to aggregate individual preference relations into a social one. Furthermore, the social interval multiplicative preference relation owns acceptable consistency when every individual one is acceptably consistent. Two numerical examples are carried out to show the efficiency of the proposed Goal Programming model and the algorithms.
Ujjwal Maulik - One of the best experts on this subject based on the ideXlab platform.
-
a Goal Programming procedure for fuzzy multiobjective linear fractional Programming problem
Fuzzy Sets and Systems, 2003Co-Authors: Bhola Nath Moitra, Ujjwal MaulikAbstract:Abstract This paper presents a Goal Programming (GP) procedure for fuzzy multiobjective linear fractional Programming (FMOLFP) problems. In the proposed approach, which is motivated by Mohamed (Fuzzy Sets and Systems 89 (1997) 215), GP model for achievement of the highest membership value of each of fuzzy Goals defined for the fractional objectives is formulated. In the solution process, the method of variable change on the under- and over- deviational variables of the membership Goals associated with the fuzzy Goals of the model is introduced to solve the problem efficiently by using linear Goal Programming (LGP) methodology. The approach is illustrated by two numerical examples.
Davide La Torre - One of the best experts on this subject based on the ideXlab platform.
-
planning sustainable development through a scenario based stochastic Goal Programming model
Operational Research, 2017Co-Authors: Raja Jayaraman, Davide La Torre, Cinzia Colapinto, Danilo LiuzziAbstract:Most real-world optimization problems involve numerous conflicting criteria, imprecise information estimates and Goals, thus the stochastic Goal Programming method offers an analytical framework to model and solve such problems. In this paper, we develop a stochastic Goal Programming model with satisfaction function that integrates optimal resource (labor) allocation to simultaneously satisfy conflicting criteria related to economic development, energy consumption, workforce allocation, and greenhouse gas emissions. We validate the model using sectorial data obtained from diverse sources on vital economic sectors for the United Arab Emirates. The results offer significant insights to decision makers for strategic planning decisions and investment allocations towards achieving long term sustainable development Goals.
-
multi criteria model for sustainable development using Goal Programming applied to the united arab emirates
Energy Policy, 2015Co-Authors: Raja Jayaraman, Davide La Torre, Cinzia Colapinto, Tufail MalikAbstract:Sustainable development requires implementing suitable policies integrating several competing objectives on economic, environmental, energy and social criteria. Multi-Criteria Decision Analysis (MCDA) using Goal Programming is a popular and widely used technique to study decision problems in the face of multiple conflicting objectives. MCDA assists policy makers by providing clarity in choosing between alternatives for strategic planning and investments. In this paper, we propose a weighted Goal Programming model that integrates efficient allocation of resources to simultaneously achieve sustainability related Goals on GDP growth, electricity consumption and GHG emissions. We validate the model with application to key economic sectors of the United Arab Emirates to achieve sustainable development Goals by the year 2030. The model solution provides a quantitative justification and a basis for comparison in planning future energy requirements and an indispensable requirement to include renewable sources to satisfy long-term energy requirements.
-
the stochastic Goal Programming model theory and applications
Post-Print, 2012Co-Authors: Belaid Aouni, Fouad Ben Abdelaziz, Davide La TorreAbstract:Supported by a network of researchers and practitioners, the Goal Programming (GP) model is alive today more than ever and is continually fed with theoretical developments and new applications with resounding success. The standard formulation of the GP model was introduced in the earliest of 1960s, and since then, important extensions and numerous applications have been proposed. One of these variants is the stochastic GP model that deals with the uncertainty of some decision-making situations by using stochastic calculus. In such a situation, the decision maker is not able to assess with certainty the different parameters. However, he or she can provide some information regarding the likelihood of occurrence of the decision-making parameter values. The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains.
-
a generalized stochastic Goal Programming model
Applied Mathematics and Computation, 2010Co-Authors: Belaid Aouni, Davide La TorreAbstract:In this paper we show how one can get stochastic solutions of Stochastic Multi-objective Problem (SMOP) using Goal Programming models. In literature it is well known that one can reduce a SMOP to deterministic equivalent problems and reduce the analysis of a stochastic problem to a collection of deterministic problems. The first sections of this paper will be devoted to the introduction of deterministic equivalent problems when the feasible set is a random set and we show how to solve them using Goal Programming technique. In the second part we try to go more in depth on notion of SMOP solution and we suppose that it has to be a random variable. We will present stochastic Goal Programming model for finding stochastic solutions of SMOP. Our approach requires more computational time than the one based on deterministic equivalent problems due to the fact that several optimization programs (which depend on the number of experiments to be run) needed to be solved. On the other hand, since in our approach we suppose that a SMOP solution is a random variable, according to the Central Limit Theorem the larger will be the sample size and the more precise will be the estimation of the statistical moments of a SMOP solution. The developed model will be illustrated through numerical examples.
